Professional December 2017/January 2018


Decision intelligence

Charles Hipps, chief executive officer and founder of WCN, welcomes the AI revolution

C onsider this. A dedicated recruiter can spend in excess of two months of their time reviewing initial candidate applications. No matter how many recruiters a company has, the time this takes totals into a huge number of months spent annually supporting just the initial review activity. Decision intelligence can radicalise this process. It offers recruiters the opportunity to take evidence-based decision making to an entirely new level by factoring in an unprecedented amount of data from a wide array of sources, some of which might never have been considered previously. Used well, practitioners can test theories, proactively solve problems and conduct more complex predictive analytics related to sourcing and hiring strategies. This isn’t necessarily something that’s in the future, it’s already here. You’re already using it in terms of your smartphones. You’re already beginning to come to a situation where you will be increasingly using that to do most aspects of what you do. The blurring of that from an employee point of view, what you do from a social point of view, and what you do in your home, is becoming one of the major features in terms of how artificial intelligence (AI) can also help. Knowing what worked well in the past can help to fine-tune the types of candidates that carry high favour within a firm. The benefits to recruitment include: ● saving recruiters’ time ● getting to the top candidates first ● finding the ‘needle-in-a-hay-stack’ ● reducing bias ● increasing diversity. So, of course, when biases are recognised it’s possible to adjust for them, but that means that with any data you’re using – and any tools that you’re using to

analyse and take decisions based on that data – you need to be asking providers very carefully about the risks of bias and how they are going to correct for that. Why is this important? In the eyes of some, the recruiting process is imperfect, elitist and, obviously, exclusionary. With decision intelligence you can get to the candidates that no one else knows about. It can be done, but the amount of effort often outweighs the reward. Currently, you might receive over 150,000 applications a year rising from a mixture of core and non-core schools – big data can ease this pressure. Used well, it will sift and flag candidates who have all the key indicators of success you’re looking for, but that didn’t go to a target school – i.e. schools that are not on anyone’s core schools lists but do have exceptional talent. ...human element should never be taken out of the equation... If you think about your own recruitment, you’re probably getting 10, 20, 50, 100 applications for every hire – which is a lot of information to process and understand so therefore employers limit the number of applications they receive. This is perfectly understandable because everyone wants to make their jobs doable, but that has some consequences which we don’t necessarily realise. At the same time, you may also limit the number of sources to recruit from. What typically happens is you get some good applications from a small number of sources and therefore consider them as a great place or source to hire from.

Consequently, we invest more into those sources and we end up hiring even more people from those sources, ignoring others unconsciously. Harnessing the potential of decision intelligence means you’re not just dismissing elitism theories but you’re also identifying and quantifying any historic bias thereby reducing bias in future decision making. The algorithms eliminate the chance of disparate treatment by not accepting protected category data and replicating collective decision making to reduce the influence of bias by individuals or process. It means you can mitigate the influence of disparate impact and focus on just winning great hires. Reporting wise, it helps with ensuring that you are providing stronger evidence and recordkeeping to support hiring decisions and can accept more applications with lower resource implications. Clever algorithms replicate your collective decision making, reducing the influence of bias by individuals or process. And yet recruiters shouldn’t fear it as a big obstacle to their careers. Remember, humans must still understand how it’s being used. Data alone will not get you a decision – you need to have data, you need to be able to generate insight and you also need to be able to link that to action. So, therefore, the human element should never be taken out of the equation as the actual technology itself is just an actual enabler. In summary, predictive recruitment analytics and big data intelligence tools are changing the way organisations view, analyse and harness their talent data. Leveraged efficiently, predictive analytics allows staffing teams to create economic value from their talent data, helping them become more competitive and successful. n

| Professional in Payroll, Pensions and Reward | December 2017/January 2018 | Issue 36 44

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